Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations50000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.8 MiB
Average record size in memory415.6 B

Variable types

Numeric14
Categorical9

Alerts

Alcohol_Consumption_Per_Week has 3325 (6.7%) zeros Zeros
Fast_Food_Intake_Per_Week has 4942 (9.9%) zeros Zeros
Processed_Food_Intake_Per_Week has 4983 (10.0%) zeros Zeros

Reproduction

Analysis started2025-03-30 00:26:02.981550
Analysis finished2025-03-30 00:26:48.332196
Duration45.35 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct72
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.3987
Minimum18
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-03-29T17:26:48.508584image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21
Q135
median53
Q371
95-th percentile86
Maximum89
Range71
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.799006
Coefficient of variation (CV)0.38950399
Kurtosis-1.2080536
Mean53.3987
Median Absolute Deviation (MAD)18
Skewness0.0042130902
Sum2669935
Variance432.59867
MonotonicityNot monotonic
2025-03-29T17:26:48.783746image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 768
 
1.5%
80 755
 
1.5%
30 753
 
1.5%
34 742
 
1.5%
61 738
 
1.5%
43 732
 
1.5%
38 730
 
1.5%
69 727
 
1.5%
65 724
 
1.4%
71 719
 
1.4%
Other values (62) 42612
85.2%
ValueCountFrequency (%)
18 682
1.4%
19 717
1.4%
20 702
1.4%
21 707
1.4%
22 711
1.4%
23 704
1.4%
24 668
1.3%
25 689
1.4%
26 707
1.4%
27 712
1.4%
ValueCountFrequency (%)
89 677
1.4%
88 690
1.4%
87 642
1.3%
86 701
1.4%
85 705
1.4%
84 689
1.4%
83 704
1.4%
82 653
1.3%
81 649
1.3%
80 755
1.5%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Female
25086 
Male
24914 

Length

Max length6
Median length6
Mean length5.00344
Min length4

Characters and Unicode

Total characters250172
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 25086
50.2%
Male 24914
49.8%

Length

2025-03-29T17:26:49.069416image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-29T17:26:49.270899image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
female 25086
50.2%
male 24914
49.8%

Most occurring characters

ValueCountFrequency (%)
e 75086
30.0%
a 50000
20.0%
l 50000
20.0%
F 25086
 
10.0%
m 25086
 
10.0%
M 24914
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 75086
30.0%
a 50000
20.0%
l 50000
20.0%
F 25086
 
10.0%
m 25086
 
10.0%
M 24914
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 75086
30.0%
a 50000
20.0%
l 50000
20.0%
F 25086
 
10.0%
m 25086
 
10.0%
M 24914
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 75086
30.0%
a 50000
20.0%
l 50000
20.0%
F 25086
 
10.0%
m 25086
 
10.0%
M 24914
 
10.0%

Ethnicity
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Other
10094 
Black
10063 
White
9982 
Hispanic
9938 
Asian
9923 

Length

Max length8
Median length5
Mean length5.59628
Min length5

Characters and Unicode

Total characters279814
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowBlack
3rd rowWhite
4th rowOther
5th rowWhite

Common Values

ValueCountFrequency (%)
Other 10094
20.2%
Black 10063
20.1%
White 9982
20.0%
Hispanic 9938
19.9%
Asian 9923
19.8%

Length

2025-03-29T17:26:49.470207image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-29T17:26:49.664904image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
other 10094
20.2%
black 10063
20.1%
white 9982
20.0%
hispanic 9938
19.9%
asian 9923
19.8%

Most occurring characters

ValueCountFrequency (%)
i 39781
14.2%
a 29924
10.7%
t 20076
 
7.2%
h 20076
 
7.2%
e 20076
 
7.2%
c 20001
 
7.1%
n 19861
 
7.1%
s 19861
 
7.1%
O 10094
 
3.6%
r 10094
 
3.6%
Other values (7) 69970
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 279814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 39781
14.2%
a 29924
10.7%
t 20076
 
7.2%
h 20076
 
7.2%
e 20076
 
7.2%
c 20001
 
7.1%
n 19861
 
7.1%
s 19861
 
7.1%
O 10094
 
3.6%
r 10094
 
3.6%
Other values (7) 69970
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 279814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 39781
14.2%
a 29924
10.7%
t 20076
 
7.2%
h 20076
 
7.2%
e 20076
 
7.2%
c 20001
 
7.1%
n 19861
 
7.1%
s 19861
 
7.1%
O 10094
 
3.6%
r 10094
 
3.6%
Other values (7) 69970
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 279814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 39781
14.2%
a 29924
10.7%
t 20076
 
7.2%
h 20076
 
7.2%
e 20076
 
7.2%
c 20001
 
7.1%
n 19861
 
7.1%
s 19861
 
7.1%
O 10094
 
3.6%
r 10094
 
3.6%
Other values (7) 69970
25.0%

Income
Real number (ℝ)

Distinct41490
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85176.555
Minimum20000
Maximum149997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-03-29T17:26:49.913783image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum20000
5-th percentile26548.75
Q152551
median85355.5
Q3117782.75
95-th percentile143680.65
Maximum149997
Range129997
Interquartile range (IQR)65231.75

Descriptive statistics

Standard deviation37574.185
Coefficient of variation (CV)0.44113295
Kurtosis-1.2032394
Mean85176.555
Median Absolute Deviation (MAD)32621
Skewness-0.0042735244
Sum4.2588278 × 109
Variance1.4118194 × 109
MonotonicityNot monotonic
2025-03-29T17:26:50.192375image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56624 5
 
< 0.1%
89091 5
 
< 0.1%
103300 5
 
< 0.1%
141417 5
 
< 0.1%
99241 5
 
< 0.1%
20948 5
 
< 0.1%
111742 5
 
< 0.1%
66399 5
 
< 0.1%
25179 4
 
< 0.1%
116513 4
 
< 0.1%
Other values (41480) 49952
99.9%
ValueCountFrequency (%)
20000 1
< 0.1%
20001 1
< 0.1%
20002 1
< 0.1%
20005 1
< 0.1%
20008 1
< 0.1%
20009 1
< 0.1%
20010 1
< 0.1%
20020 2
< 0.1%
20021 1
< 0.1%
20025 1
< 0.1%
ValueCountFrequency (%)
149997 2
< 0.1%
149994 1
< 0.1%
149992 1
< 0.1%
149988 1
< 0.1%
149985 1
< 0.1%
149979 1
< 0.1%
149975 1
< 0.1%
149974 1
< 0.1%
149972 2
< 0.1%
149969 1
< 0.1%

BMI
Real number (ℝ)

Distinct266
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.818748
Minimum18.5
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-03-29T17:26:50.455003image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum18.5
5-th percentile19.9
Q125.2
median31.8
Q338.4
95-th percentile43.7
Maximum45
Range26.5
Interquartile range (IQR)13.2

Descriptive statistics

Standard deviation7.6371385
Coefficient of variation (CV)0.24002008
Kurtosis-1.1968168
Mean31.818748
Median Absolute Deviation (MAD)6.6
Skewness-0.0061229154
Sum1590937.4
Variance58.325884
MonotonicityNot monotonic
2025-03-29T17:26:50.723715image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.3 234
 
0.5%
25.4 224
 
0.4%
38.3 223
 
0.4%
27.9 220
 
0.4%
27.2 219
 
0.4%
36.2 218
 
0.4%
37.8 217
 
0.4%
27.5 217
 
0.4%
38.7 216
 
0.4%
24.1 215
 
0.4%
Other values (256) 47797
95.6%
ValueCountFrequency (%)
18.5 93
0.2%
18.6 179
0.4%
18.7 177
0.4%
18.8 156
0.3%
18.9 199
0.4%
19 171
0.3%
19.1 177
0.4%
19.2 183
0.4%
19.3 184
0.4%
19.4 186
0.4%
ValueCountFrequency (%)
45 118
0.2%
44.9 198
0.4%
44.8 177
0.4%
44.7 202
0.4%
44.6 183
0.4%
44.5 174
0.3%
44.4 206
0.4%
44.3 234
0.5%
44.2 205
0.4%
44.1 197
0.4%

Blood_Pressure
Real number (ℝ)

Distinct901
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.08096
Minimum90
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-03-29T17:26:51.003942image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile94.6
Q1112.4
median135.2
Q3157.6
95-th percentile175.6
Maximum180
Range90
Interquartile range (IQR)45.2

Descriptive statistics

Standard deviation26.039637
Coefficient of variation (CV)0.1927706
Kurtosis-1.2045785
Mean135.08096
Median Absolute Deviation (MAD)22.6
Skewness-0.0048002203
Sum6754047.8
Variance678.06269
MonotonicityNot monotonic
2025-03-29T17:26:51.281857image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
173.8 79
 
0.2%
131.9 78
 
0.2%
94.6 77
 
0.2%
139.6 77
 
0.2%
157.6 76
 
0.2%
161.9 76
 
0.2%
108.4 75
 
0.1%
148.1 75
 
0.1%
142 75
 
0.1%
156.8 74
 
0.1%
Other values (891) 49238
98.5%
ValueCountFrequency (%)
90 31
0.1%
90.1 43
0.1%
90.2 47
0.1%
90.3 56
0.1%
90.4 56
0.1%
90.5 52
0.1%
90.6 55
0.1%
90.7 64
0.1%
90.8 65
0.1%
90.9 56
0.1%
ValueCountFrequency (%)
180 29
0.1%
179.9 49
0.1%
179.8 47
0.1%
179.7 60
0.1%
179.6 49
0.1%
179.5 60
0.1%
179.4 63
0.1%
179.3 64
0.1%
179.2 60
0.1%
179.1 55
0.1%

Cholesterol
Real number (ℝ)

Distinct2001
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.18502
Minimum100
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-03-29T17:26:51.541798image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile110.2
Q1150.4
median200.3
Q3250.3
95-th percentile290.3
Maximum300
Range200
Interquartile range (IQR)99.9

Descriptive statistics

Standard deviation57.737684
Coefficient of variation (CV)0.2884216
Kurtosis-1.1971703
Mean200.18502
Median Absolute Deviation (MAD)49.9
Skewness-0.0016506092
Sum10009251
Variance3333.6402
MonotonicityNot monotonic
2025-03-29T17:26:51.842614image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
207.2 41
 
0.1%
158.1 41
 
0.1%
100.1 40
 
0.1%
255.9 40
 
0.1%
225 39
 
0.1%
124.7 39
 
0.1%
102.4 39
 
0.1%
264.7 39
 
0.1%
116.3 39
 
0.1%
185.3 39
 
0.1%
Other values (1991) 49604
99.2%
ValueCountFrequency (%)
100 10
 
< 0.1%
100.1 40
0.1%
100.2 30
0.1%
100.3 20
< 0.1%
100.4 24
< 0.1%
100.5 25
0.1%
100.6 19
< 0.1%
100.7 28
0.1%
100.8 29
0.1%
100.9 27
0.1%
ValueCountFrequency (%)
300 13
 
< 0.1%
299.9 29
0.1%
299.8 24
< 0.1%
299.7 28
0.1%
299.6 20
< 0.1%
299.5 24
< 0.1%
299.4 34
0.1%
299.3 26
0.1%
299.2 17
< 0.1%
299.1 25
0.1%

Exercise_Hours_Per_Week
Real number (ℝ)

Distinct101
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.991036
Minimum0
Maximum10
Zeros238
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-03-29T17:26:52.403686image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q12.5
median5
Q37.5
95-th percentile9.5
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.882748
Coefficient of variation (CV)0.5775851
Kurtosis-1.202015
Mean4.991036
Median Absolute Deviation (MAD)2.5
Skewness0.004888033
Sum249551.8
Variance8.3102363
MonotonicityNot monotonic
2025-03-29T17:26:52.727683image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 559
 
1.1%
8.1 549
 
1.1%
8.2 543
 
1.1%
2.8 534
 
1.1%
6.4 533
 
1.1%
1.4 529
 
1.1%
7.4 529
 
1.1%
3 528
 
1.1%
2 527
 
1.1%
7.9 527
 
1.1%
Other values (91) 44642
89.3%
ValueCountFrequency (%)
0 238
0.5%
0.1 510
1.0%
0.2 491
1.0%
0.3 483
1.0%
0.4 494
1.0%
0.5 484
1.0%
0.6 510
1.0%
0.7 492
1.0%
0.8 489
1.0%
0.9 511
1.0%
ValueCountFrequency (%)
10 254
0.5%
9.9 469
0.9%
9.8 525
1.1%
9.7 462
0.9%
9.6 518
1.0%
9.5 488
1.0%
9.4 483
1.0%
9.3 512
1.0%
9.2 499
1.0%
9.1 461
0.9%

Alcohol_Consumption_Per_Week
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.00484
Minimum0
Maximum14
Zeros3325
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-03-29T17:26:52.942198image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q311
95-th percentile14
Maximum14
Range14
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.324922
Coefficient of variation (CV)0.6174191
Kurtosis-1.2108456
Mean7.00484
Median Absolute Deviation (MAD)4
Skewness0.0020917789
Sum350242
Variance18.704951
MonotonicityNot monotonic
2025-03-29T17:26:53.158061image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
14 3432
 
6.9%
9 3393
 
6.8%
4 3371
 
6.7%
2 3364
 
6.7%
6 3341
 
6.7%
10 3338
 
6.7%
8 3337
 
6.7%
3 3335
 
6.7%
12 3326
 
6.7%
0 3325
 
6.7%
Other values (5) 16438
32.9%
ValueCountFrequency (%)
0 3325
6.7%
1 3310
6.6%
2 3364
6.7%
3 3335
6.7%
4 3371
6.7%
5 3295
6.6%
6 3341
6.7%
7 3277
6.6%
8 3337
6.7%
9 3393
6.8%
ValueCountFrequency (%)
14 3432
6.9%
13 3283
6.6%
12 3326
6.7%
11 3273
6.5%
10 3338
6.7%
9 3393
6.8%
8 3337
6.7%
7 3277
6.6%
6 3341
6.7%
5 3295
6.6%

Smoking_Status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Never
16878 
Former
16613 
Current
16509 

Length

Max length7
Median length6
Mean length5.99262
Min length5

Characters and Unicode

Total characters299631
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNever
2nd rowNever
3rd rowFormer
4th rowNever
5th rowNever

Common Values

ValueCountFrequency (%)
Never 16878
33.8%
Former 16613
33.2%
Current 16509
33.0%

Length

2025-03-29T17:26:53.448424image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-29T17:26:53.647067image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
never 16878
33.8%
former 16613
33.2%
current 16509
33.0%

Most occurring characters

ValueCountFrequency (%)
r 83122
27.7%
e 66878
22.3%
N 16878
 
5.6%
v 16878
 
5.6%
F 16613
 
5.5%
o 16613
 
5.5%
m 16613
 
5.5%
C 16509
 
5.5%
u 16509
 
5.5%
n 16509
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 299631
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 83122
27.7%
e 66878
22.3%
N 16878
 
5.6%
v 16878
 
5.6%
F 16613
 
5.5%
o 16613
 
5.5%
m 16613
 
5.5%
C 16509
 
5.5%
u 16509
 
5.5%
n 16509
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 299631
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 83122
27.7%
e 66878
22.3%
N 16878
 
5.6%
v 16878
 
5.6%
F 16613
 
5.5%
o 16613
 
5.5%
m 16613
 
5.5%
C 16509
 
5.5%
u 16509
 
5.5%
n 16509
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 299631
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 83122
27.7%
e 66878
22.3%
N 16878
 
5.6%
v 16878
 
5.6%
F 16613
 
5.5%
o 16613
 
5.5%
m 16613
 
5.5%
C 16509
 
5.5%
u 16509
 
5.5%
n 16509
 
5.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
25096 
0
24904 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 25096
50.2%
0 24904
49.8%

Length

2025-03-29T17:26:53.837462image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-29T17:26:53.992828image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 25096
50.2%
0 24904
49.8%

Most occurring characters

ValueCountFrequency (%)
1 25096
50.2%
0 24904
49.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 25096
50.2%
0 24904
49.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 25096
50.2%
0 24904
49.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 25096
50.2%
0 24904
49.8%

Glucose_Level
Real number (ℝ)

Distinct1301
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.01539
Minimum70
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-03-29T17:26:54.208308image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile76.5
Q1102.5
median135.3
Q3167.3
95-th percentile193.4
Maximum200
Range130
Interquartile range (IQR)64.8

Descriptive statistics

Standard deviation37.458042
Coefficient of variation (CV)0.27743534
Kurtosis-1.1976423
Mean135.01539
Median Absolute Deviation (MAD)32.3
Skewness-0.0036722552
Sum6750769.7
Variance1403.1049
MonotonicityNot monotonic
2025-03-29T17:26:54.479244image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
183.9 59
 
0.1%
142.2 59
 
0.1%
163.8 58
 
0.1%
184.4 57
 
0.1%
122.9 56
 
0.1%
190.2 55
 
0.1%
120.5 55
 
0.1%
161.7 55
 
0.1%
167.3 55
 
0.1%
163.4 54
 
0.1%
Other values (1291) 49437
98.9%
ValueCountFrequency (%)
70 20
< 0.1%
70.1 38
0.1%
70.2 42
0.1%
70.3 40
0.1%
70.4 28
0.1%
70.5 41
0.1%
70.6 48
0.1%
70.7 32
0.1%
70.8 36
0.1%
70.9 38
0.1%
ValueCountFrequency (%)
200 13
 
< 0.1%
199.9 39
0.1%
199.8 36
0.1%
199.7 30
0.1%
199.6 27
0.1%
199.5 36
0.1%
199.4 42
0.1%
199.3 30
0.1%
199.2 45
0.1%
199.1 43
0.1%

HbA1c
Real number (ℝ)

Distinct61
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.999164
Minimum4
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-03-29T17:26:54.734705image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.3
Q15.5
median7
Q38.5
95-th percentile9.7
Maximum10
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7275405
Coefficient of variation (CV)0.24682098
Kurtosis-1.1955706
Mean6.999164
Median Absolute Deviation (MAD)1.5
Skewness0.0035951187
Sum349958.2
Variance2.9843962
MonotonicityNot monotonic
2025-03-29T17:26:54.982786image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.1 941
 
1.9%
6.3 888
 
1.8%
7.8 878
 
1.8%
6.8 874
 
1.7%
7.3 872
 
1.7%
5.3 868
 
1.7%
6.1 863
 
1.7%
8.9 860
 
1.7%
8.7 858
 
1.7%
5.5 857
 
1.7%
Other values (51) 41241
82.5%
ValueCountFrequency (%)
4 422
0.8%
4.1 784
1.6%
4.2 812
1.6%
4.3 826
1.7%
4.4 810
1.6%
4.5 836
1.7%
4.6 818
1.6%
4.7 824
1.6%
4.8 843
1.7%
4.9 807
1.6%
ValueCountFrequency (%)
10 396
0.8%
9.9 833
1.7%
9.8 849
1.7%
9.7 807
1.6%
9.6 833
1.7%
9.5 807
1.6%
9.4 841
1.7%
9.3 801
1.6%
9.2 848
1.7%
9.1 851
1.7%

Insulin_Resistance
Real number (ℝ)

Distinct91
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.510322
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-03-29T17:26:55.263405image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q13.3
median5.5
Q37.7
95-th percentile9.6
Maximum10
Range9
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation2.5940473
Coefficient of variation (CV)0.47076147
Kurtosis-1.1889947
Mean5.510322
Median Absolute Deviation (MAD)2.2
Skewness0.0010374209
Sum275516.1
Variance6.7290814
MonotonicityNot monotonic
2025-03-29T17:26:55.506565image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.2 611
 
1.2%
9.7 610
 
1.2%
3.9 603
 
1.2%
5.3 597
 
1.2%
6.6 596
 
1.2%
1.8 594
 
1.2%
5 590
 
1.2%
8 589
 
1.2%
1.5 587
 
1.2%
7.3 583
 
1.2%
Other values (81) 44040
88.1%
ValueCountFrequency (%)
1 269
0.5%
1.1 532
1.1%
1.2 575
1.1%
1.3 530
1.1%
1.4 552
1.1%
1.5 587
1.2%
1.6 523
1.0%
1.7 513
1.0%
1.8 594
1.2%
1.9 531
1.1%
ValueCountFrequency (%)
10 304
0.6%
9.9 561
1.1%
9.8 573
1.1%
9.7 610
1.2%
9.6 551
1.1%
9.5 568
1.1%
9.4 555
1.1%
9.3 557
1.1%
9.2 548
1.1%
9.1 542
1.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
25106 
1
24894 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 25106
50.2%
1 24894
49.8%

Length

2025-03-29T17:26:55.719547image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-29T17:26:55.896580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 25106
50.2%
1 24894
49.8%

Most occurring characters

ValueCountFrequency (%)
0 25106
50.2%
1 24894
49.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 25106
50.2%
1 24894
49.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 25106
50.2%
1 24894
49.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 25106
50.2%
1 24894
49.8%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Low
16789 
High
16657 
Moderate
16554 

Length

Max length8
Median length4
Mean length4.98854
Min length3

Characters and Unicode

Total characters249427
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowModerate
3rd rowLow
4th rowHigh
5th rowModerate

Common Values

ValueCountFrequency (%)
Low 16789
33.6%
High 16657
33.3%
Moderate 16554
33.1%

Length

2025-03-29T17:26:56.135598image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-29T17:26:56.317660image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
low 16789
33.6%
high 16657
33.3%
moderate 16554
33.1%

Most occurring characters

ValueCountFrequency (%)
o 33343
13.4%
e 33108
13.3%
L 16789
 
6.7%
w 16789
 
6.7%
H 16657
 
6.7%
i 16657
 
6.7%
g 16657
 
6.7%
h 16657
 
6.7%
M 16554
 
6.6%
d 16554
 
6.6%
Other values (3) 49662
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 249427
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 33343
13.4%
e 33108
13.3%
L 16789
 
6.7%
w 16789
 
6.7%
H 16657
 
6.7%
i 16657
 
6.7%
g 16657
 
6.7%
h 16657
 
6.7%
M 16554
 
6.6%
d 16554
 
6.6%
Other values (3) 49662
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 249427
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 33343
13.4%
e 33108
13.3%
L 16789
 
6.7%
w 16789
 
6.7%
H 16657
 
6.7%
i 16657
 
6.7%
g 16657
 
6.7%
h 16657
 
6.7%
M 16554
 
6.6%
d 16554
 
6.6%
Other values (3) 49662
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 249427
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 33343
13.4%
e 33108
13.3%
L 16789
 
6.7%
w 16789
 
6.7%
H 16657
 
6.7%
i 16657
 
6.7%
g 16657
 
6.7%
h 16657
 
6.7%
M 16554
 
6.6%
d 16554
 
6.6%
Other values (3) 49662
19.9%

Fast_Food_Intake_Per_Week
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.50898
Minimum0
Maximum9
Zeros4942
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-03-29T17:26:56.505822image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8631527
Coefficient of variation (CV)0.634989
Kurtosis-1.2180108
Mean4.50898
Median Absolute Deviation (MAD)2
Skewness-0.0052977174
Sum225449
Variance8.1976433
MonotonicityNot monotonic
2025-03-29T17:26:56.676667image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 5076
10.2%
8 5067
10.1%
4 5064
10.1%
6 5050
10.1%
7 5015
10.0%
5 4975
10.0%
2 4954
9.9%
0 4942
9.9%
1 4934
9.9%
9 4923
9.8%
ValueCountFrequency (%)
0 4942
9.9%
1 4934
9.9%
2 4954
9.9%
3 5076
10.2%
4 5064
10.1%
5 4975
10.0%
6 5050
10.1%
7 5015
10.0%
8 5067
10.1%
9 4923
9.8%
ValueCountFrequency (%)
9 4923
9.8%
8 5067
10.1%
7 5015
10.0%
6 5050
10.1%
5 4975
10.0%
4 5064
10.1%
3 5076
10.2%
2 4954
9.9%
1 4934
9.9%
0 4942
9.9%

Processed_Food_Intake_Per_Week
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.48826
Minimum0
Maximum9
Zeros4983
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-03-29T17:26:56.847602image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8730032
Coefficient of variation (CV)0.64011514
Kurtosis-1.2258852
Mean4.48826
Median Absolute Deviation (MAD)2
Skewness0.0086616571
Sum224413
Variance8.2541473
MonotonicityNot monotonic
2025-03-29T17:26:57.051632image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 5125
10.2%
4 5070
10.1%
1 5054
10.1%
9 5004
10.0%
5 4991
10.0%
0 4983
10.0%
7 4978
10.0%
8 4961
9.9%
6 4934
9.9%
3 4900
9.8%
ValueCountFrequency (%)
0 4983
10.0%
1 5054
10.1%
2 5125
10.2%
3 4900
9.8%
4 5070
10.1%
5 4991
10.0%
6 4934
9.9%
7 4978
10.0%
8 4961
9.9%
9 5004
10.0%
ValueCountFrequency (%)
9 5004
10.0%
8 4961
9.9%
7 4978
10.0%
6 4934
9.9%
5 4991
10.0%
4 5070
10.1%
3 4900
9.8%
2 5125
10.2%
1 5054
10.1%
0 4983
10.0%

Daily_Caloric_Intake
Real number (ℝ)

Distinct2800
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2604.6124
Minimum1200
Maximum3999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-03-29T17:26:57.316381image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1344
Q11911
median2604
Q33298
95-th percentile3862.05
Maximum3999
Range2799
Interquartile range (IQR)1387

Descriptive statistics

Standard deviation807.34038
Coefficient of variation (CV)0.30996565
Kurtosis-1.1947462
Mean2604.6124
Median Absolute Deviation (MAD)694
Skewness-0.0050990851
Sum1.3023062 × 108
Variance651798.48
MonotonicityNot monotonic
2025-03-29T17:26:57.551632image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2348 32
 
0.1%
3698 31
 
0.1%
3709 30
 
0.1%
1207 30
 
0.1%
1904 30
 
0.1%
1382 30
 
0.1%
2895 30
 
0.1%
3682 30
 
0.1%
1972 30
 
0.1%
3272 29
 
0.1%
Other values (2790) 49698
99.4%
ValueCountFrequency (%)
1200 12
 
< 0.1%
1201 15
< 0.1%
1202 14
< 0.1%
1203 16
< 0.1%
1204 14
< 0.1%
1205 15
< 0.1%
1206 16
< 0.1%
1207 30
0.1%
1208 13
< 0.1%
1209 12
 
< 0.1%
ValueCountFrequency (%)
3999 20
< 0.1%
3998 15
< 0.1%
3997 16
< 0.1%
3996 16
< 0.1%
3995 25
0.1%
3994 16
< 0.1%
3993 19
< 0.1%
3992 21
< 0.1%
3991 20
< 0.1%
3990 18
< 0.1%

Sleep_Hours_Per_Night
Real number (ℝ)

Distinct61
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.000978
Minimum4
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-03-29T17:26:57.837600image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.3
Q15.5
median7
Q38.5
95-th percentile9.7
Maximum10
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7364959
Coefficient of variation (CV)0.24803619
Kurtosis-1.2059917
Mean7.000978
Median Absolute Deviation (MAD)1.5
Skewness0.0028996246
Sum350048.9
Variance3.015418
MonotonicityNot monotonic
2025-03-29T17:26:58.107335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 903
 
1.8%
9.6 894
 
1.8%
5.4 889
 
1.8%
5.9 880
 
1.8%
6.6 880
 
1.8%
5.7 878
 
1.8%
8 876
 
1.8%
4.2 871
 
1.7%
6.3 870
 
1.7%
8.9 864
 
1.7%
Other values (51) 41195
82.4%
ValueCountFrequency (%)
4 408
0.8%
4.1 849
1.7%
4.2 871
1.7%
4.3 842
1.7%
4.4 792
1.6%
4.5 800
1.6%
4.6 807
1.6%
4.7 833
1.7%
4.8 857
1.7%
4.9 819
1.6%
ValueCountFrequency (%)
10 400
0.8%
9.9 854
1.7%
9.8 859
1.7%
9.7 808
1.6%
9.6 894
1.8%
9.5 903
1.8%
9.4 776
1.6%
9.3 833
1.7%
9.2 853
1.7%
9.1 819
1.6%

Stress_Level
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Low
16734 
Moderate
16672 
High
16594 

Length

Max length8
Median length4
Mean length4.99908
Min length3

Characters and Unicode

Total characters249954
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowLow
3rd rowHigh
4th rowLow
5th rowHigh

Common Values

ValueCountFrequency (%)
Low 16734
33.5%
Moderate 16672
33.3%
High 16594
33.2%

Length

2025-03-29T17:26:58.340216image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-29T17:26:58.511836image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
low 16734
33.5%
moderate 16672
33.3%
high 16594
33.2%

Most occurring characters

ValueCountFrequency (%)
o 33406
13.4%
e 33344
13.3%
L 16734
 
6.7%
w 16734
 
6.7%
M 16672
 
6.7%
d 16672
 
6.7%
r 16672
 
6.7%
a 16672
 
6.7%
t 16672
 
6.7%
H 16594
 
6.6%
Other values (3) 49782
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 249954
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 33406
13.4%
e 33344
13.3%
L 16734
 
6.7%
w 16734
 
6.7%
M 16672
 
6.7%
d 16672
 
6.7%
r 16672
 
6.7%
a 16672
 
6.7%
t 16672
 
6.7%
H 16594
 
6.6%
Other values (3) 49782
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 249954
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 33406
13.4%
e 33344
13.3%
L 16734
 
6.7%
w 16734
 
6.7%
M 16672
 
6.7%
d 16672
 
6.7%
r 16672
 
6.7%
a 16672
 
6.7%
t 16672
 
6.7%
H 16594
 
6.6%
Other values (3) 49782
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 249954
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 33406
13.4%
e 33344
13.3%
L 16734
 
6.7%
w 16734
 
6.7%
M 16672
 
6.7%
d 16672
 
6.7%
r 16672
 
6.7%
a 16672
 
6.7%
t 16672
 
6.7%
H 16594
 
6.6%
Other values (3) 49782
19.9%

Medication_Use
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
25044 
0
24956 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 25044
50.1%
0 24956
49.9%

Length

2025-03-29T17:26:58.686980image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-29T17:26:58.841645image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 25044
50.1%
0 24956
49.9%

Most occurring characters

ValueCountFrequency (%)
1 25044
50.1%
0 24956
49.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 25044
50.1%
0 24956
49.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 25044
50.1%
0 24956
49.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 25044
50.1%
0 24956
49.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
25104 
0
24896 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 25104
50.2%
0 24896
49.8%

Length

2025-03-29T17:26:59.018678image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-29T17:26:59.183876image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 25104
50.2%
0 24896
49.8%

Most occurring characters

ValueCountFrequency (%)
1 25104
50.2%
0 24896
49.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 25104
50.2%
0 24896
49.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 25104
50.2%
0 24896
49.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 25104
50.2%
0 24896
49.8%

Interactions

2025-03-29T17:26:44.426913image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:09.292124image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:11.996734image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:14.827183image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:17.394427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:19.615070image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:22.166083image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:24.959977image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:27.769356image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:30.314731image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:33.559077image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:36.576750image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:39.119196image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:41.567299image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:44.619811image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:09.512822image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:12.184424image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:15.036617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:17.561961image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:19.810769image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:22.577317image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:25.175971image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:27.945426image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:30.544019image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:33.784212image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:36.748478image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:39.289118image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:41.729721image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:44.826103image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:09.696737image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:12.410721image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:15.248194image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:17.724253image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:19.995331image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:22.771395image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:25.361338image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:28.160239image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:30.796368image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:34.088491image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:36.911463image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:39.465714image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:41.929803image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:45.000880image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:09.866981image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:12.596902image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:15.459645image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:17.871544image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:20.190687image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:22.991259image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:25.535395image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:28.324449image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:31.023164image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:34.367510image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:37.077409image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:39.649619image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:42.112304image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:45.168477image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:10.023075image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:12.767552image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:15.629736image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:18.002289image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:20.349051image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:23.172046image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:25.724805image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:28.494506image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:31.235195image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:34.569557image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:37.251173image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:39.826174image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:42.284128image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:45.362437image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:10.245866image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:12.960685image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:15.828916image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:18.175515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:20.528195image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:23.408147image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:25.951387image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:28.730441image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:31.464439image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:34.863773image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:37.516450image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:40.016512image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:42.474155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:45.535514image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:10.473273image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:13.147926image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:15.999629image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:18.348233image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:20.687420image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:23.578862image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:26.132166image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:28.894731image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:31.682783image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:35.074371image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:37.690013image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:40.177856image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:42.646231image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:45.760297image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:10.677607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:13.354912image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:16.215749image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:18.505234image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:20.875637image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:23.763309image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:26.346794image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:29.061059image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:31.907505image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:35.288336image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:37.886432image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:40.364657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:42.842591image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:45.939285image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:10.876505image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:13.536463image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:16.391037image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:18.651165image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:21.046446image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:23.937340image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:26.596236image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:29.220093image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:32.328914image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:35.476448image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:38.060866image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:40.570277image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:43.032448image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:46.142365image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:11.069738image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:13.869565image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:16.582469image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:18.827191image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:21.226640image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:24.117009image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:26.791436image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:29.415492image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:32.532971image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:35.662487image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:38.240699image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:40.769165image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:43.421423image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:46.369061image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:11.260266image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:14.065940image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:16.750205image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:18.988540image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:21.409340image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:24.292962image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:26.959731image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:29.565479image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:32.754542image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:35.850153image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:38.420029image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:40.933325image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:43.603834image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:46.556958image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:11.433361image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:14.254100image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:16.911863image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:19.116859image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:21.599078image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:24.448270image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:27.134663image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:29.718457image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:32.938075image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:36.029561image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:38.596874image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:41.097024image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:43.793387image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:46.739543image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:11.640611image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:14.423387image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:17.086910image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:19.260342image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:21.789355image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:24.605286image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:27.348558image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:29.892096image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:33.125928image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:36.219814image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:38.774498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:41.246329image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:43.973624image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:46.912173image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:11.817355image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:14.612954image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:17.249260image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:19.415629image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:21.983103image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:24.772776image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:27.546155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:30.077584image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:33.342737image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:36.411636image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:38.965199image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:41.414724image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T17:26:44.179564image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-03-29T17:26:59.530637image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
AgeAlcohol_Consumption_Per_WeekBMIBlood_PressureCholesterolDaily_Caloric_IntakeDiabetes_DiagnosisEthnicityExercise_Hours_Per_WeekFamily_History_DiabetesFast_Food_Intake_Per_WeekGenderGlucose_LevelHbA1cHeart_Disease_HistoryIncomeInsulin_ResistanceMedication_UsePhysical_Activity_LevelProcessed_Food_Intake_Per_WeekSleep_Hours_Per_NightSmoking_StatusStress_Level
Age1.0000.005-0.0020.0010.000-0.0000.0110.003-0.0080.000-0.0010.012-0.000-0.0010.007-0.0000.0020.0000.000-0.0010.0000.0000.000
Alcohol_Consumption_Per_Week0.0051.0000.0020.0090.0020.0010.0000.0000.0010.000-0.0040.016-0.0100.0010.000-0.0030.0010.0000.0000.0070.0040.0000.000
BMI-0.0020.0021.000-0.003-0.0030.0040.0000.000-0.0040.000-0.0050.0000.0010.0020.0000.0080.0030.0140.0080.0020.0020.0030.000
Blood_Pressure0.0010.009-0.0031.000-0.004-0.0010.0000.0040.0000.006-0.0020.008-0.0010.0000.0000.002-0.0010.0030.000-0.006-0.0010.0070.000
Cholesterol0.0000.002-0.003-0.0041.0000.0030.0000.0070.0040.000-0.0030.006-0.0040.0040.007-0.0070.0100.0000.011-0.003-0.0030.0000.005
Daily_Caloric_Intake-0.0000.0010.004-0.0010.0031.0000.0080.0000.0020.000-0.0040.0120.0060.0020.013-0.002-0.0040.0000.0000.0010.0030.0070.010
Diabetes_Diagnosis0.0110.0000.0000.0000.0000.0081.0000.0020.0060.0050.0000.0040.0000.0030.0000.0000.0000.0030.0000.0000.0050.0020.000
Ethnicity0.0030.0000.0000.0040.0070.0000.0021.0000.0040.0050.0000.0010.0000.0000.0000.0000.0000.0000.0000.0020.0000.0020.006
Exercise_Hours_Per_Week-0.0080.001-0.0040.0000.0040.0020.0060.0041.0000.0040.0050.0090.006-0.0010.009-0.0060.0050.0100.000-0.0000.0040.0050.004
Family_History_Diabetes0.0000.0000.0000.0060.0000.0000.0050.0050.0041.0000.0060.0060.0000.0000.0070.0120.0110.0000.0000.0000.0000.0060.000
Fast_Food_Intake_Per_Week-0.001-0.004-0.005-0.002-0.003-0.0040.0000.0000.0050.0061.0000.001-0.0010.0020.009-0.002-0.0010.0000.004-0.002-0.0020.0000.002
Gender0.0120.0160.0000.0080.0060.0120.0040.0010.0090.0060.0011.0000.0000.0070.0010.0000.0000.0030.0000.0000.0000.0000.000
Glucose_Level-0.000-0.0100.001-0.001-0.0040.0060.0000.0000.0060.000-0.0010.0001.000-0.0010.0000.003-0.0080.0050.000-0.005-0.0020.0000.000
HbA1c-0.0010.0010.0020.0000.0040.0020.0030.000-0.0010.0000.0020.007-0.0011.0000.0000.0010.0000.0040.000-0.000-0.0070.0000.000
Heart_Disease_History0.0070.0000.0000.0000.0070.0130.0000.0000.0090.0070.0090.0010.0000.0001.0000.0100.0000.0020.0000.0000.0110.0000.000
Income-0.000-0.0030.0080.002-0.007-0.0020.0000.000-0.0060.012-0.0020.0000.0030.0010.0101.0000.0010.0000.0000.004-0.0010.0090.000
Insulin_Resistance0.0020.0010.003-0.0010.010-0.0040.0000.0000.0050.011-0.0010.000-0.0080.0000.0000.0011.0000.0070.0000.002-0.0020.0060.009
Medication_Use0.0000.0000.0140.0030.0000.0000.0030.0000.0100.0000.0000.0030.0050.0040.0020.0000.0071.0000.0000.0000.0080.0090.005
Physical_Activity_Level0.0000.0000.0080.0000.0110.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0001.0000.0030.0000.0000.000
Processed_Food_Intake_Per_Week-0.0010.0070.002-0.006-0.0030.0010.0000.002-0.0000.000-0.0020.000-0.005-0.0000.0000.0040.0020.0000.0031.000-0.0080.0070.007
Sleep_Hours_Per_Night0.0000.0040.002-0.001-0.0030.0030.0050.0000.0040.000-0.0020.000-0.002-0.0070.011-0.001-0.0020.0080.000-0.0081.0000.0000.006
Smoking_Status0.0000.0000.0030.0070.0000.0070.0020.0020.0050.0060.0000.0000.0000.0000.0000.0090.0060.0090.0000.0070.0001.0000.000
Stress_Level0.0000.0000.0000.0000.0050.0100.0000.0060.0040.0000.0020.0000.0000.0000.0000.0000.0090.0050.0000.0070.0060.0001.000

Missing values

2025-03-29T17:26:47.252955image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-29T17:26:47.887136image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeGenderEthnicityIncomeBMIBlood_PressureCholesterolExercise_Hours_Per_WeekAlcohol_Consumption_Per_WeekSmoking_StatusFamily_History_DiabetesGlucose_LevelHbA1cInsulin_ResistanceHeart_Disease_HistoryPhysical_Activity_LevelFast_Food_Intake_Per_WeekProcessed_Food_Intake_Per_WeekDaily_Caloric_IntakeSleep_Hours_Per_NightStress_LevelMedication_UseDiabetes_Diagnosis
069FemaleOther3955738.294.6252.93.34Never0101.06.15.10Low3436418.7Low01
132MaleBlack9066333.6167.0282.64.67Never0178.14.41.71Moderate8033618.0Low10
289MaleWhite11618039.4100.6106.86.15Former1184.48.34.91Low9823978.8High10
378MaleOther7305940.6111.1169.77.49Never0126.77.49.80High7524916.4Low11
438FemaleWhite3538929.7143.3296.52.66Never1199.98.51.71Moderate0713256.6High10
541MaleWhite2652022.5179.9118.70.50Never1173.67.14.00Moderate0015136.4High00
620MaleWhite3019418.5167.8178.20.65Former1137.07.32.00Moderate8320086.9Low11
739FemaleAsian7266842.0167.8105.86.311Never1125.08.71.80High3639946.4Low11
870MaleHispanic9734832.9156.8122.14.07Former178.46.38.51Low3125435.7Low00
919FemaleHispanic4209932.1152.9255.91.72Never1160.76.26.90Moderate2420358.6Low11
AgeGenderEthnicityIncomeBMIBlood_PressureCholesterolExercise_Hours_Per_WeekAlcohol_Consumption_Per_WeekSmoking_StatusFamily_History_DiabetesGlucose_LevelHbA1cInsulin_ResistanceHeart_Disease_HistoryPhysical_Activity_LevelFast_Food_Intake_Per_WeekProcessed_Food_Intake_Per_WeekDaily_Caloric_IntakeSleep_Hours_Per_NightStress_LevelMedication_UseDiabetes_Diagnosis
4999045FemaleBlack9484441.0148.3288.58.72Current1113.67.74.60Low8318196.4Moderate11
4999166MaleOther6528734.4153.4106.42.913Former0177.94.21.51High7019339.8Moderate01
4999262FemaleHispanic11177839.1120.2193.51.310Current1190.19.41.41Moderate4131689.1High10
4999377FemaleOther8257038.997.6178.88.20Never1192.27.47.31Low7128548.4Low10
4999473MaleBlack5322141.396.0119.46.512Never1145.88.48.01Moderate5723067.2High00
4999521MaleBlack9921620.8100.9263.39.96Current0143.68.86.41Moderate1538124.3Moderate01
4999635FemaleAsian6840442.6138.0220.82.614Never075.24.65.20High3939359.3Moderate01
4999746MaleWhite2133744.9179.2211.17.79Current0173.08.49.51Moderate5225797.4Moderate10
4999856MaleOther9376024.6179.4292.17.96Never1198.19.07.70Low6019516.2High00
4999972MaleWhite10054135.5102.6128.21.29Never0140.95.77.01High7420924.8Moderate10